{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# KEGGutils Tutorial 3 : `KEGGpathway`s\n",
    "## .... how to deal with KEGG Pathways\n",
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"../img/logo_cut.png\" alt=\"logo\" style=\"width: 300px;\"/>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### First things first\n",
    "Let's make sure we imported `KEGGutils` ( and that we can find it in scope if we did not install it )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os,sys,inspect\n",
    "currentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))\n",
    "parentdir = os.path.dirname(currentdir)\n",
    "sys.path.insert(0,parentdir)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import KEGGutils as kg\n",
    "import networkx as nx"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "and that that we have the last available version"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'0.3.2'"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kg.__version__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "let's remove the cached files to use the freshest available data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "> deleting the following files from /home/phil/repos/KEGGutils/tutorials/kegg_downloads\n",
      "/home/phil/repos/KEGGutils/tutorials/kegg_downloads/hsa05215-description\n",
      "/home/phil/repos/KEGGutils/tutorials/kegg_downloads/hsa05215-kgml\n"
     ]
    }
   ],
   "source": [
    "kg.delete_cached_files()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### What are `KEGGpathway`s?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the previous tutorial we've talked about `KEGGgraph`s and some of the derived classes like `KEGGlinkgraph` and `KEGGchain`, in this tutorial we'll see another derived class called `KEGGpathway`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Most of **KEGG** importance derives from it's large catalogue of mapped pathways which helps to store and visualize the acquired knowledge on various types of processes, from metabolism, to disease mapping to drug developement. \n",
    "\n",
    "KEGG pathways are extremely etherogeneous in form and content and KEGG makes them available to the public either in their online website visualization or in form of *KGML files*: *KGML files* are .xml files with standardized in *KEGG Markup Language* which is a xml protocol particular to KEGG, you can find some infos on that [here](https://www.kegg.jp/kegg/xml/).\n",
    "\n",
    "KEGGutils manages can deal with all of the .xml file downloading, parsing and visualization for you with the help of the `KEGGpathway` class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KEGG Pathway:\n",
      "    Extends the NetworkX.DiGraph(), includes a parser to read KGML .xml files \n",
      "    \n",
      "    Parameters\n",
      "    ----------\n",
      "    kgml_file : str\n",
      "        location of a KGML .xml file to parse\n",
      "    pathway_id (optional) : str\n",
      "        pathway id for downloading from KEGG\n",
      "    tree (optional) : xml tree\n",
      "            xml tree for the patway\n",
      "        \n",
      "\n",
      "    Properties\n",
      "    ----------\n",
      "    title : str\n",
      "        title of the pathway\n",
      "    labels: dict\n",
      "        dictionary of node labels\n",
      "    idcode: str\n",
      "        id code of the pathway\n",
      "    tree: XML ElementTree\n",
      "        element tree for the KGML file\n",
      "    pos: dict\n",
      "        dictionary of graph node positions for plotting\n",
      "    nodedict: dict\n",
      "        dictionary of ENTRY occurrences, ordered by corresponding graph node\n",
      "    kgml_file: str\n",
      "        path to KGML .xml file to parse\n",
      "    kegg_image: img\n",
      "        pathway image downloaded from KEGG\n",
      "    reactions: dict\n",
      "        dictionary of reactions in the pathway, ordered by corresponding graph node\n",
      "    genes: dict\n",
      "        dictionary of genes in the pathway, ordered by corresponding graph node\n",
      "    maps: dict\n",
      "        dictionary of maps in the pathway, ordered by corresponding graph node\n",
      "    orthologs: dict\n",
      "        dictionary of orthologs in the pathway, ordered by corresponding graph node\n",
      "    enzymes: dict\n",
      "        dictionary of enzymes in the pathway, ordered by corresponding graph node\n",
      "    groups: dict\n",
      "        dictionary of groups in the pathway, ordered by corresponding graph node\n",
      "    brite_nodes: dict\n",
      "        dictionary of brite-style nodes in the pathway, ordered by corresponding graph node\n",
      "    other_nodes: dict\n",
      "        dictionary of unclassified nodes in the pathway, ordered by corresponding graph node\n",
      "    relations: dict\n",
      "        dictionary of relations in the pathway, ordered by corresponding graph edge\n",
      "    weblink : str\n",
      "        link to KEGG pathway page\n",
      "    elements: dict\n",
      "        dictionary of the previous dictionaries ordered by nodetype\n",
      "    element_keys: list\n",
      "        keys for elements\n",
      "    \n",
      "    Methods:\n",
      "    --------\n",
      "\n",
      "    KEGGpathway.parse_kgml(kgml_file):\n",
      "        parses a kgml file\n",
      "    KEGGpathway.draw():\n",
      "        Plots the pathway with networkx.draw_networkx() (note: KGML file format        is missing lot of additional information, for visualization purposes is        reccomended to use downloaded kegg images)\n",
      "    KEGGpathway.download_img():\n",
      "        Downloads the pathway visualization from KEGG and returns it\n",
      "    KEGGpathway.list_by_nodetype(nodetype):\n",
      "        Returns a list of corresponding nodetypes in the pathway.\n",
      "    KEGGpathway.get_references():\n",
      "        Returns a list of academic references to given pathway\n",
      "    \n"
     ]
    }
   ],
   "source": [
    "print(kg.KEGGpathway.__doc__)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's build our first `KEGGpathway`: we'll start from selecting a pathway identifier from the **KEGG** database (note that you can provide your own KGML file instead or directly use an XML tree object if it's compatible with KGML standard)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Infos on hsa05215 from KEGG:\n",
      "\n",
      "NAME        Prostate cancer - Homo sapiens (human)\n",
      "DESCRIPTION Prostate cancer constitutes a major health problem in Western countries. It is the most frequently diagnosed cancer among men and the second leading cause of male cancer deaths. The identification of key molecular alterations in prostate-cancer cells implicates carcinogen defenses (GSTP1), growth-factor-signaling pathways (NKX3.1, PTEN, and p27), and androgens (AR) as critical determinants of the phenotype of prostate-cancer cells. Glutathione S-transferases (GSTP1) are detoxifying enzymes. Cells of prostatic intraepithelial neoplasia, devoid of GSTP1, undergo genomic damage mediated by carcinogens. NKX3.1, PTEN, and p27 regulate the growth and survival of prostate cells in the normal prostate. Inadequate levels of PTEN and NKX3.1 lead to a reduction in p27 levels and to increased proliferation and decreased apoptosis. Androgen receptor (AR) is a transcription factor that is normally activated by its androgen ligand. During androgen withdrawal therapy, the AR signal transduction pathway also could be activated by amplification of the AR gene, by AR gene mutations, or by altered activity of AR coactivators. Through these mechanisms, tumor cells lead to the emergence of androgen-independent prostate cancer.\n",
      "CLASS       Human Diseases; Cancer: specific types\n"
     ]
    }
   ],
   "source": [
    "kg.get_infos(\"hsa05215\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "and then we just need to initialize a `KEGGpathway` object passing `pathway_id` to it's constructor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Prostate cancer\n"
     ]
    }
   ],
   "source": [
    "pathway = kg.KEGGpathway(pathway_id = \"hsa05215\")\n",
    "print(pathway.title)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`KEGGpathway`s always have a `weblink` attribute to make the integration with **KEGG** online webservices as seamless as possible"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "http://www.kegg.jp/kegg-bin/show_pathway?hsa05215\n"
     ]
    }
   ],
   "source": [
    "print(pathway.weblink)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The XML tree is accessible via the `tree` attribute, in case you need to pass it on the fly to another tool"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xml.etree.ElementTree.ElementTree at 0x7efba8d03b90>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pathway.tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'name': 'path:hsa05215',\n",
       " 'org': 'hsa',\n",
       " 'number': '05215',\n",
       " 'title': 'Prostate cancer',\n",
       " 'image': 'http://www.kegg.jp/kegg/pathway/hsa/hsa05215.png',\n",
       " 'link': 'http://www.kegg.jp/kegg-bin/show_pathway?hsa05215'}"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pathway.tree.getroot().attrib"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To get a list of available publication references you can use the `kg.get_references()` on any KEGG identifier, for`KEGGpathway`s, you can just call the `get_references()` method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'reference_hook': 'PMID:12878745',\n",
       "  'authors': 'Nelson WG, De Marzo AM, Isaacs WB.',\n",
       "  'title': 'Prostate cancer.',\n",
       "  'journal': 'DOI:10.1056/NEJMra021562'},\n",
       " {'reference_hook': 'PMID:15724144',\n",
       "  'authors': 'Li L, Ittmann MM, Ayala G, Tsai MJ, Amato RJ, Wheeler TM, Miles BJ, Kadmon D, Thompson TC.',\n",
       "  'title': 'The emerging role of the PI3-K-Akt pathway in prostate cancer progression.',\n",
       "  'journal': 'DOI:10.1038/sj.pcan.4500776'},\n",
       " {'reference_hook': 'PMID:16551847',\n",
       "  'authors': 'Pienta KJ, Bradley D.',\n",
       "  'title': 'Mechanisms underlying the development of androgen-independent prostate cancer.',\n",
       "  'journal': 'DOI:10.1158/1078-0432.CCR-06-0067'},\n",
       " {'reference_hook': 'PMID:11900250',\n",
       "  'authors': 'Feldman BJ, Feldman D.',\n",
       "  'title': 'The development of androgen-independent prostate cancer.',\n",
       "  'journal': 'DOI:10.1038/35094009'},\n",
       " {'reference_hook': 'PMID:15082523',\n",
       "  'authors': 'Heinlein CA, Chang C.',\n",
       "  'title': 'Androgen receptor in prostate cancer.',\n",
       "  'journal': 'DOI:10.1210/er.2002-0032'}]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "references = pathway.get_references()\n",
    "\n",
    "references[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`KEGGpathway`s intrinsecally  `KEGGgraph`s and mantain all the attributes and methods `KEGGgraph`s have, like the `get_unique_nodetypes()` method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['compound', 'gene', 'group', 'map']"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pathway.get_unique_nodetypes()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "with an addition to some special dictionaries, like `genes` and `relations` that give you quick access to particular nodetypes and edges"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'gene': 'hsa:1385',\n",
       " 'description': 'CREB3, LUMAN, LZIP, sLZIP...',\n",
       " 'nodetype': 'gene',\n",
       " 'kegglink': 'http://www.kegg.jp/dbget-bin/www_bget?hsa:10488+hsa:1385+hsa:148327+hsa:468+hsa:64764+hsa:84699+hsa:90993+hsa:9586'}"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pathway.genes['38']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'nodes': ('hsa:2932', 'hsa:1385'),\n",
       " 'node_ids': ('46', '38'),\n",
       " 'relation_type': 'PPrel',\n",
       " 'subtypes': [('inhibition', '--|'), ('phosphorylation', '+p')]}"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pathway.relations['46to38']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Those dictionaries help us to navigate through `KEGGpathway` nodes and edges"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'relation_type': 'PPrel',\n",
       " 'subtypes': [('inhibition', '--|'), ('phosphorylation', '+p')]}"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "node_ids = pathway.relations['46to38']['node_ids']\n",
    "\n",
    "pathway.edges[node_ids]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Additional dictionaries to help us plot the graph are provided, like a dict of graphical coordinates for each node"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(734, -616)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pathway.pos['38']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And a list of labels to facilitate plotting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'CREB3, LUMAN, LZIP, sLZIP...'"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pathway.labels['38']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Some really basic pathway plotting is provided, but results are still in an alpha-state"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pathway.draw()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Please beware that KGML file standard is not perfect for graphical representation as it often misses some important descriptors, for preliminary visualization I recommend using KEGG images when available, those can be downloaded using the `download_img()` method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:root:> Downloading path-hsa05215-image from KEGG at http://rest.kegg.jp/get/path:hsa05215/image\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pathway.download_img()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "An url o the same image can be returned using the `return_url` option, or simply using the `imagelink` attribute"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "using pathway.download_img() : http://www.kegg.jp/kegg/pathway/hsa/hsa05215.png\n",
      "using pathway.imagelink : http://www.kegg.jp/kegg/pathway/hsa/hsa05215.png\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"using pathway.download_img() : \" + pathway.download_img(return_url = True))\n",
    "print(\"using pathway.imagelink : \" + pathway.imagelink)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And this concludes the short tutorial on `KEGGpathway` and the series of four tutorials: now it's about using and seeing what you can do with it."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The project is, of course, open to contributions and new features will be added in the near future, stay updated on the project's status on the GitHub page https://github.com/filippocastelli/KEGGutils/"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
